



import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


__UpperCAmelCase			  =			['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']



class 							lowerCAmelCase_						(    a__				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=1			)	->						List[str]:
          UpperCamelCase       :    int		      =      tokenizer
          UpperCamelCase       :    List[str]		      =      dataset
          UpperCamelCase       :    Optional[Any]		      =      len(SCREAMING_SNAKE_CASE_			) if n_tasks is None else n_tasks
          UpperCamelCase       :    str		      =      n_copies






       def __iter__(     self			)	->						Tuple:
          UpperCamelCase       :    List[str]		      =      []
          for task in range(self.n_tasks			):
             # without strip, the model generate commented codes ...
             prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip()			)
          UpperCamelCase       :    Any		      =      self.tokenizer(SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			)
          for task in range(self.n_tasks			):
             for _ in range(self.n_copies			):
                yield {
                    "ids": outputs.input_ids[task],
                    "task_id": task,
                    "input_len": outputs.attention_mask[task].sum(),
                }






class 							lowerCAmelCase_						(    a__				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          UpperCamelCase       :    Any		      =      start_length
          UpperCamelCase       :    Optional[int]		      =      eof_strings
          UpperCamelCase       :    List[Any]		      =      tokenizer






       def __call__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      self.tokenizer.batch_decode(input_ids[:, self.start_length :]			)
          UpperCamelCase       :    List[Any]		      =      []
          for decoded_generation in decoded_generations:
             done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings			)			)
          return all(SCREAMING_SNAKE_CASE_			)






def 						UpperCamelCase      (  snake_case__			:       Any      )      ->	List[str]:
   UpperCamelCase       :    Tuple		      =      re.split('(%s)' % '|'.join(snake_case__      )			,	snake_case__      )
   # last string should be ""
   return "".join(string_list[:-2]      )
def 						UpperCamelCase      (  snake_case__			:       List[str]			,	snake_case__			:       List[str]			,	snake_case__			:       Optional[int]			,	snake_case__			:       Optional[int]			,	snake_case__			:       Tuple			,	snake_case__			:       Optional[Any]=20			,	**snake_case__			:       List[str]      )      ->	Tuple:
   UpperCamelCase       :    Union[str, Any]		      =      defaultdict(snake_case__      )  # dict of list of generated tokens
   for step, batch in tqdm(enumerate(snake_case__      )      ):
      with torch.no_grad():
         UpperCamelCase       :    Optional[int]		      =      batch['ids'].shape[-1]
         UpperCamelCase       :    Union[str, Any]		      =      accelerator.unwrap_model(snake_case__      ).generate(
             input_ids=batch['ids'][:, : batch['input_len']]			,	num_return_sequences=snake_case__			,	**snake_case__      )
         # each task is generated batch_size times
         UpperCamelCase       :    Optional[Any]		      =      batch['task_id'].repeat(snake_case__      )
         UpperCamelCase       :    Any		      =      accelerator.pad_across_processes(
             snake_case__			,	dim=1			,	pad_index=tokenizer.pad_token_id      )

         UpperCamelCase      ,	UpperCamelCase       :    Any		      =      accelerator.gather((generated_tokens, generated_tasks)      )
         UpperCamelCase       :    Optional[int]		      =      generated_tokens.cpu().numpy()
         UpperCamelCase       :    str		      =      generated_tasks.cpu().numpy()

         for task, generated_tokens in zip(snake_case__			,	snake_case__      ):
            gen_token_dict[task].append(snake_case__      )

   UpperCamelCase       :    Tuple		      =      [[] for _ in range(snake_case__      )]
   for task, generated_tokens in gen_token_dict.items():
      for s in generated_tokens:
         UpperCamelCase       :    Optional[Any]		      =      tokenizer.decode(snake_case__			,	skip_special_tokens=snake_case__			,	clean_up_tokenization_spaces=snake_case__      )
         code_gens[task].append(remove_last_block(snake_case__      )      )
   return code_gens
def 						UpperCamelCase      (  )      ->	Any:
   # Setup configuration
   UpperCamelCase       :    Optional[Any]		      =      HfArgumentParser(snake_case__      )
   UpperCamelCase       :    Optional[Any]		      =      parser.parse_args()

   transformers.logging.set_verbosity_error()
   # enables code execution in code_eval metric
   UpperCamelCase       :    Dict		      =      args.HF_ALLOW_CODE_EVAL
   # make sure tokenizer plays nice with multiprocessing
   UpperCamelCase       :    Optional[int]		      =      'false'

   if args.num_workers is None:
      UpperCamelCase       :    Union[str, Any]		      =      multiprocessing.cpu_count()

   # Use dataset load to feed to accelerate
   UpperCamelCase       :    List[Any]		      =      Accelerator()
   set_seed(args.seed			,	device_specific=snake_case__      )

   # Load model and tokenizer
   UpperCamelCase       :    Dict		      =      AutoTokenizer.from_pretrained(args.model_ckpt      )
   UpperCamelCase       :    List[str]		      =      tokenizer.eos_token
   UpperCamelCase       :    List[Any]		      =      AutoModelForCausalLM.from_pretrained(args.model_ckpt      )

   # Generation settings
   UpperCamelCase       :    Optional[Any]		      =      {
       'do_sample': args.do_sample,
       'temperature': args.temperature,
       'max_new_tokens': args.max_new_tokens,
       'top_p': args.top_p,
       'top_k': args.top_k,
       'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0			,	snake_case__			,	snake_case__      )]      ),
   }

   # Load evaluation dataset and metric
   UpperCamelCase       :    Dict		      =      load_dataset('openai_humaneval'      )
   UpperCamelCase       :    List[str]		      =      load_metric('code_eval'      )

   UpperCamelCase       :    Optional[int]		      =      args.num_tasks if args.num_tasks is not None else len(human_eval['test']      )
   UpperCamelCase       :    Union[str, Any]		      =      args.n_samples // args.batch_size

   UpperCamelCase       :    int		      =      TokenizedDataset(snake_case__			,	human_eval['test']			,	n_copies=snake_case__			,	n_tasks=snake_case__      )
   # do not confuse args.batch_size, which is actually the num_return_sequences
   UpperCamelCase       :    Tuple		      =      DataLoader(snake_case__			,	batch_size=1      )

   # Run a quick test to see if code evaluation is enabled
   try:
      UpperCamelCase       :    int		      =      code_eval_metric.compute(references=['']			,	predictions=[['']]      )
   except ValueError as exception:
      print(
          'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
          ' flag to enable code evaluation.'      )
      raise exception

   UpperCamelCase      ,	UpperCamelCase       :    Tuple		      =      accelerator.prepare(snake_case__			,	snake_case__      )

   UpperCamelCase       :    List[Any]		      =      complete_code(
       snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	n_tasks=snake_case__			,	batch_size=args.batch_size			,	**snake_case__			,	)

   if accelerator.is_main_process:
      UpperCamelCase       :    int		      =      []

      for task in tqdm(range(snake_case__      )      ):
         UpperCamelCase       :    Tuple		      =      human_eval['test'][task]['test']
         UpperCamelCase       :    str		      =      F"""check({human_eval["test"][task]["entry_point"]})"""
         references.append('\n' + test_func + '\n' + entry_point      )

      # Evaluate completions with "code_eval" metric
      UpperCamelCase      ,	UpperCamelCase       :    str		      =      code_eval_metric.compute(
          references=snake_case__			,	predictions=snake_case__			,	num_workers=args.num_workers      )
      print(F"""Results: {pass_at_k}"""      )

      # Save results to json file
      with open(args.output_file			,	'w'      ) as fp:
         json.dump(snake_case__			,	snake_case__      )


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
    main()










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



